The Center for Mathematics and Applications (NOVA Math) invites you to the NOVA Math Thematic Weeks 2023, with an emphasis on Mathematical models for Health, jointly organized by the NOVA Math’s Thematics Lines, Biomathematics, Mathematics for Health, and Data Science.
The event is intended for students, researchers, and professionals interested in extending their knowledge on the mathematical methods, computational tools, and technologies covered. Participants at the interface between mathematics, computer, and health and medical sciences will have the opportunity to attend seminars on the underlying theory and concepts, practical hands-on sessions using the R language, and participate in informal discussions.
Seminar 1, 6 July, 15:00, Seminar Room, 2nd Floor, Building VII
Alexandra Posekany, Research Unit of Computational Statistics, Vienna University of Technology
Bayesian and robust insights in data analysis and classification of health data
Seminar 2, 13 July, 15:00, Room 1.16, 1st Floor, Building VII
Sonia Torazona, Applied Statistics and Operations Research and Quality, Universitat Politècnica de València
Approaching disease through omics data: challenges and opportunities
Seminar 3, 13 July, 16:00, Room 1.16, 1st Floor, Building VII
Antonio Gómez Corral, Complutense University of Madrid
On the exact measure of the disease spread in SIS epidemic models with horizontal and vertical transmission
Workshop 1, 5, 6 July, 10:00 – 12:00, Seminar Room, 2nd Floor, Building VII
Alexandra Posekany, Research Unit of Computational Statistics, Vienna University of Technology
Bayesian inference in medical statistics and epidemiology: a brief introduction
Workshop 2, 11, 12 July, 10:00 – 12:00, Room 1.16, 1st Floor, Building VII
Antonio Gómez Corral, Complutense University of Madrid
Markov Chains in Epidemiology
Workshop 3, 11, 12 July, 14:00 – 16:00, Room 1.16 1st Floor, Building VII
Sonia Torazona, Applied Statistics and Operations Research and Quality, Universitat Politècnica de València
Bioinformatic tools for multi-omics analysis